Classification of Mouse Chromosomes Using Artificial Neural Networks

该方案的具体目标是利用新型人工神经网络（ann）的自适应能力和并行计算特性对小鼠染色体进行自动分类。总体目标是提供一个计算机软件系统来自动化小鼠核型过程，从而显著减少必要的时间和人力，并提高分析的质量和效率。小鼠染色体的分析对遗传学研究的许多领域都很重要。示例应用包括通过核酸探针对小鼠染色体进行原位杂交进行基因定位，物质筛选需要在使用该物质处理后对大量动物（最常见的是小鼠）进行染色体分析，以及测试育种者以维持携带感兴趣的染色体畸变的小鼠的库存。老鼠的染色体比人类的染色体更难分类。虽然自动化的人类核型系统是可用的，但没有一个成功地用于自动分类小鼠染色体。人工神经网络（ann）是一门植根于众多学科的新兴技术。人工神经网络是一种信息处理系统，具有与生物神经网络相同的性能特征。人工神经网络已经发展成为人类认知或神经生物学数学模型的概括。人工神经网络由网络架构和学习范式组成。该体系结构定义了网络的拓扑结构和复杂性，以获得感兴趣领域中特定级别的知识，在本例中是对特定染色体的识别。学习范式包括在网络中嵌入知识的训练过程。我们提出设计、训练、测试和评估两种新的人工神经网络，径向基函数（RBF）和概率神经网络（PNN），用于小鼠染色体分类。优越的人工神经网络将集成到现有的核型软件分布中，并将取代传统的分类模块。利用现有的核型软件，我们可以通过提供在光学显微镜下捕获中期扩散、分割和增强数字化图像以及用户界面的工具，专注于分类的重要问题。为实现本提案的目标，具体计划如下：1。获得小鼠染色体2的原始图像。识别小鼠染色体的独特特征3 .开发特征自动提取程序；准备培训和测试数据集；设计、训练和测试径向基函数（RBF）神经网络分类器；设计、训练和测试概率神经网络（PNN）分类器，7。8.选择最优的小鼠染色体人工神经网络分类器；提高分类性能；建立小鼠核型系统。

The specific goal of this proposal is to use adaptive capabilities and parallel computational properties of novel artificial neural networks (ANNs) for automatic classification of mouse chromosomes. The overall objective is to provide a computer software system to automate the mouse karyotyping process, thus signifcantly reducing the time and human effort neccessary, and improving the quality and efficiency of analysis. Analysis of mouse chormosomes is important to many fields of genetic research. Example applications include gene mapping by in situ hybridization to nucleic acid probes to mouse chromosomes, substance screening which requires chromosome analysis of large numbers of animals (most drequently mice) after treatment with the substance, and testing breeders to maintain stocks of mice that carry chromosome aberrations of interest. Mouse chromosomes are significantly more difficult to classify than human chromosomes. Although automated human karyotyping systems are available, none have been used successfully to classify mouse chromosomes automatically. Artificial Neural Networks (ANNs) represent an emerging technology rooted in many disciplines. An ANN is an information processing system that has certain performance characteristics in common with biological neural networks. ANNs have been developed as generalizations of mathematical models of human cognition or neural biology. An ANN consist of a network architecture and a learning paradigm. The architecture defines the topology and complexity of the network necessary to acquire a specific level of knowledge in the area of interest, which in this case is recognition of specific chromosomes. The learning paradigm involves a training process for embedding the knowledge in the network. We propose to design, train, test, and eveluate two novel ANNs, Radial Basis Function (RBF) and Probabilistic Neural Network (PNN), for classification of mouse chromosomes. The superior ANN will be integrated into an existing karyotyping software distribution and will replace the conventional classification module. Use of the existing karyotyping software allows us to concentrate on the important issue of classification by providing the tools for capture of metaphase spreads under a light microscope , segmentatiion and enhancement of digitized images, as well as a user interface. To achieve the objective of this porposal, the specific plans are to 1. obtain raw images of mouse chromosomes 2. identify distinctive features of mouse chromosomes 3. develop the programs for automatic extraction of the features, 4. prepare the training and testing data sets, 5. design, train, and test the radial basis function (RBF) neural network classifier, 6. design, train, and test the probabilistic neural network (PNN) classifier, 7. select the optimal artificial neural network classifier for mouse chromosomes, 8. improve the classification performance, 9. develop the mouse karyotyping qystem.